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Abstract #0915

The Effect of Model-Based and Data-Driven Physiological Noise Correction Techniques on the Degree of Clustering in Resting-State fMRI Functional Connectivity

Michalis Kassinopoulos1 and Georgios D. Mitsis2

1Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada, 2Department of Bioengineering, McGill University, Montreal, QC, Canada

One of the most essential steps in the analysis pipeline of fMRI studies is the correction for fluctuations due to physiological processes and head motion. This is particularly relevant for resting-state fMRI functional connectivity (FC) studies, where the SNR is lower and physiological fluctuations may introduce common variance in the signals from different areas of the brain, inflating FC. Several physiological noise correction techniques have been developed over the years. Nevertheless, an optimal preprocessing pipeline for FC has not yet been established. In this study, we examined more than 400 different pipelines using both model-based and data-driven techniques and have found that tissue-based regressors significantly improve the identifiability of well-known resting-state networks.

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